Import pandas, PCA and Standard Scaler

PCA can give you wonky results if the variance in the original dataset is large, so we want to standardize the data. StandardScaler allows you to standardize the dataset so the mean is 0 and the variance is 1. This process is common in ML models

import pandas as pd
import plotly.express as px

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

Load iris and take a look at the dataset

url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
df = pd.read_csv(url, names=['sepal_length','sepal_width','petal_length','petal_width','target'])
df.head()

	sepal_length	sepal_width	petal_length	petal_width	target
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa

Split the dataset into X and y

X = df.drop('target', 1)
y = df.target

Apply the standardization to the X values

X = StandardScaler().fit_transform(X)
print(pd.DataFrame(X))

            0         1         2         3
0   -0.900681  1.032057 -1.341272 -1.312977
1   -1.143017 -0.124958 -1.341272 -1.312977
2   -1.385353  0.337848 -1.398138 -1.312977
3   -1.506521  0.106445 -1.284407 -1.312977
4   -1.021849  1.263460 -1.341272 -1.312977
..        ...       ...       ...       ...
145  1.038005 -0.124958  0.819624  1.447956
146  0.553333 -1.281972  0.705893  0.922064
147  0.795669 -0.124958  0.819624  1.053537
148  0.432165  0.800654  0.933356  1.447956
149  0.068662 -0.124958  0.762759  0.790591

[150 rows x 4 columns]

Run the principal component analysis model on X

pca = PCA(n_components=2)
principalComponents = pca.fit_transform(X)
pcaDF = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2'])
pcaDF

	PC1	PC2
0	-2.264542	0.505704
1	-2.086426	-0.655405
2	-2.367950	-0.318477
3	-2.304197	-0.575368
4	-2.388777	0.674767
...	...	...
145	1.870522	0.382822
146	1.558492	-0.905314
147	1.520845	0.266795
148	1.376391	1.016362
149	0.959299	-0.022284

150 rows × 2 columns

Combine the principalDf with y to get a dataframe with both the components and y

finalDf = pd.concat([pcaDF, y], axis = 1)
finalDf

	PC1	PC2	target
0	-2.264542	0.505704	Iris-setosa
1	-2.086426	-0.655405	Iris-setosa
2	-2.367950	-0.318477	Iris-setosa
3	-2.304197	-0.575368	Iris-setosa
4	-2.388777	0.674767	Iris-setosa
...	...	...	...
145	1.870522	0.382822	Iris-virginica
146	1.558492	-0.905314	Iris-virginica
147	1.520845	0.266795	Iris-virginica
148	1.376391	1.016362	Iris-virginica
149	0.959299	-0.022284	Iris-virginica

150 rows × 3 columns

Plot the principal components vs each other (don't need to code this yourselves)

px.scatter(finalDf, x = 'PC1', y='PC2', color = 'target')

Take a look at the variance ratios for the two principal components

pca.explained_variance_ratio_

array([0.72770452, 0.23030523])